Research in the field of data mining is yielding incredible results. Companies are able to make effective use of massive data generating every day. One of the most noteworthy utilization is the practice of user modeling. After making unprecedented success in improving search engines, product research, and marketing campaigns, user modeling is making gains in introducing expert systems.
The primary goal of user modeling is the assessment of consumer behavior to comprehend specific needs. Data scientists use various techniques of artificial intelligence depending on their objectives.
Read user modeling algorithms for specific details on relevant data mining algorithms.
Following is the most accepted classification of types of user modeling.
Invariable User Modeling
This is the simplest kind of user model which does not change over time. User requirements vary with changing trends. Most of the consumers tend to adapt to the changes in market. However, there are some features that remained unchanged. Data scientists use Invariable or “Static” model to represent such features.
Since analysts do not intend any modification in future, invariable modeling does not require the application of learning algorithms.
Flexible User Modeling
In direct contrast to the invariable modeling, flexible or dynamic user modeling undergoes changes with time. There are cases where static modeling becomes insufficient owing to the diversity of users’ needs. Usually, Unsupervised Learning algorithms are used for the purpose because scientists do not want to confine the results in prefixed boundaries.
It is notable that the updated information carries a significant amount of preexisting data. The modification adds more information by making relevant grouping between the interdependent datasets.
Demographic User Modeling
This is one of the approaches of user modeling which requires classification of users in terms of their demographic statuses. Also labeled as “stereotype modeling”, this classification considers the age groups, ethnic groups, socioeconomic classes, literacy rates apart from other parameters. Individual attributes do not affect stereotypical models because it relies on large statistical sets.
The resultant model includes attributes from both flexible and invariable modeling. Demographic classification generalizes the static model as the latter is highly individual specific. It is far more reusable because invariable model represents the needs of particular users rather than a diverse set of population.
It also features attributes of a dynamic model for its ability to integrate updated trends among users. Unlike invariable model, Demographic model modifies as per the consumer needs.
Adaptive User Modeling
Contrary to Stereotype modeling, adaptive approach customizes the model as per consumers’ particular choices. It tailors the attributes to individual users. Consequently, modeling such users require a considerably greater amount of information. Nevertheless, scientists are able to predict more precisely about an individual user’s behavior.
After a series of events related to data breaches, users are voicing concerns over adaptive modeling. The violation resulted in modeling of social media users to determine their political inclination. These results enabled Cambridge Analytica – the breaching party – to manipulate the voters in the US presidential election. Thus, the development of a comprehensive regulatory framework is underway to prevent such an event in future.
Each of these modeling techniques is applied in a number of ways in various industries. At times, scientists use a combination of these techniques. Mostly, they apply each of these algorithms independently as per the circumstances.
Four most prominent applications are leaving a significant impact on industrial expansion.
The search engine service providers are the primary stakeholders of Flexible User Modeling. Social media user modeling makes recommendation to the users owing to these systems.
For instance, Facebook recommends tagging the person in a picture by prediction. Since the dynamic model trains itself over time, the suggestions keep on improving with time. Similarly, Google ranks the pages higher which are more aligned to the user’s previous searches.
Moreover, the rise of on-demand industry requires highly adaptive user modeling to suggest the most appropriate options. On-demand taxi service or grocery apps present relevant examples. Considering the previous buying or riding trends, these apps share the closest suggestions.
This application of highly adaptive user models enables the replacement of humans with software for various tasks. It asks structured questions to users and tailors the results to a specific query. Thus, an expert system solves the users’ problems by replacing a human counterpart.
These systems are the reason behind smartphone voice assistants including Siri and Cortana. Each of these assistants requires natural language processing (NLP) to understand the pronunciation of words.
The application in healthcare is revolutionizing the sector. Software systems trained by Supervised Learning are able to diagnose disease and suggest the appropriate prescription. Voice assistant is an additional feature. In usual circumstances, an expert system answers the typed queries.
Digital Marketing Campaigns
Search engines, corporate websites and social media networks have a wide range of users. However, not every user intends to purchase a product or seek services. The marketing managers aim to target the appropriate audience by modeling the users.
Facebook alone has over 2.2 billion monthly active users. A company aiming to launch a product in some specific region of the world does not have to promote the products to entire Facebook user-base. The marketers promote the product by assessing the publicly available information in the users’ profile. This practice also enables them in modeling user journey.
Product Development and Service Management
Product research is the prerequisite to development or manufacturing. It is highly unfortunate scenario when an efficient product fails to acquire a considerable user-base. Primarily, such failures occur due to lack of research. The success of products mainly depends on demand. Similarly, services in a region also rely on demand among consumers.
End user modeling allows business development managers to assess consumer needs and develop a corresponding product or service. Owing to this practice, the percentage of annual failed startups is significantly reducing.
Despite reaching the end of opening decades two of current millennia, some businesses continue the practices of previous century. Labeling competition as the reason in case of subsequent failure is a foolish approach. Companies need to ensure that they sufficiently understand their users by undertaking robust user modeling.
A comprehensive user model for your company’s potential customers is only one step away. Contact us today to allow the expert team at Mob Inspire to assist you.